adaptive_test {IRTest} | R Documentation |
Ability parameter estimation with fixed item parameters
Description
Ability parameter estimation when item responses and item parameters are given. This function can be useful in ability parameter estimation is adaptive testing.
Usage
adaptive_test(
response,
item,
model = "dich",
ability_method = "EAP",
quad = NULL,
prior = NULL
)
Arguments
response |
A matrix of item responses. For mixed-format test, a list of item responses where dichotomous item responses are the first element and polytomous item responses are the second element. |
item |
A matrix of item parameters. For mixed-format test, a list of item parameters where dichotomous item parameters are the first element and polytomous item parameters are the second element. |
model |
|
ability_method |
The ability parameter estimation method.
The available options are Expected a posteriori ( |
quad |
A vector of quadrature points for |
prior |
A vector of the prior distribution for |
Value
theta |
The estimated ability parameter values. If |
theta_se |
The standard errors of ability parameter estimates.
It returns standard deviations of posteriors for |
Author(s)
Seewoo Li cu@yonsei.ac.kr
Examples
# dichotomous
response <- c(1,1,0)
item <- matrix(
c(
1, -0.5, 0,
1.5, -1, 0,
1.2, 0, 0.2
), nrow = 3, byrow = TRUE
)
adaptive_test(response, item, model = "dich", ability_method = "WLE")
# polytomous
response <- c(1,2,0)
item <- matrix(
c(
1, -0.5, 0.5,
1.5, -1, 0,
1.2, 0, 0.4
), nrow = 3, byrow = TRUE
)
adaptive_test(response, item, model="GPCM", ability_method = "WLE")
# mixed-format test
response <- list(c(0,0,0),c(2,2,1))
item <- list(
matrix(
c(
1, -0.5, 0,
1.5, -1, 0,
1.2, 0, 0
), nrow = 3, byrow = TRUE
),
matrix(
c(
1, -0.5, 0.5,
1.5, -1, 0,
1.2, 0, 0.4
), nrow = 3, byrow = TRUE
)
)
adaptive_test(response, item, model = "GPCM", ability_method = "WLE")
# continuous response
response <- c(0.88, 0.68, 0.21)
item <- matrix(
c(
1, -0.5, 10,
1.5, -1, 8,
1.2, 0, 11
), nrow = 3, byrow = TRUE
)
adaptive_test(response, item, model = "cont", ability_method = "WLE")